Deep Learning in the Formation of Students' Nationalist Attitudes: A Bibliometric Analysis Study - Muhammad Rijal Fadli; Bahtiar Afwan; Alfian; Imam Santosa (The Indonesian Institute of Science and Technology Research, 2026)
This study addresses the limited role of deep learning in fostering students’ national pride, as existing research largely emphasizes technical aspects of artificial intelligence rather than value-based education. This gap highlights the need for integrating national values and character development into deep learning research. The study aims to examine publication trends, research directions, and patterns of scientific collaboration related to deep learning and student nationalism in the digital era. A bibliometric approach was employed to analyze scholarly publications indexed in Google Scholar, Scopus, and Web of Science (WoS) from 2019 to 2025. The analysis focused on research growth, thematic connections, author productivity, and keyword co-occurrence to map the development of the field. The findings indicate a significant increase in publications during the early period, followed by a decline in recent years, suggesting a possible shift or saturation in research focus. The most frequent keywords identified were attitudes (228), teacher (120), and deep learning (96), alongside related terms such as value, artificial intelligence, critical thinking, university, and student learning. These results reveal that deep learning research remains predominantly oriented toward general technological and pedagogical applications, with limited emphasis on cultivating national values and pride. This study underscores the need for future research that integrates deep learning with value-based education to strengthen students’ national awareness and character development in higher education contexts.